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Welcome to your Jupyter Book
Core Modules
Module 1: Python Primers
1.1 Python Basics
1.2 Intermediate Python
Module 2: Introduction
2.1 PubChem Training Course
2.2 PubChem PUG-REST
Module 3: Survey of Chemical Databases
Public Compound Databases
Module 4: Chemical Representation
4.1 Chemical Representations and Introduction to RDKit
4.2 PubChem 3D
Substructure Searching with SMARTS
Module 10: Supervised Machine Learning
10.0: Introduction
10.1: Data Preparation and Feature Engineering
10.2: Naive Bayes and Model Construction
10.3: Evaluating and Interpreting Models
10.4: Pipelines, Persistence, and Reproducible Inference
10.5 Random Forest and Model Complexity
10.6 Logistic Regression and SVM
10.7 Neural Networks
Appendices
Appendix 1: Getting Set Up
Appendix 1.1: Linux & WSL
1. What SMARTS is
Appendix 10: Supervised Machine Learning
A10.1 BioAssay Screening: Enough Actives
A10.2 Bayes’ Theorem: From Inference to Models
Repository
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Index
